The Impact of Artificial Intelligence on the Development of Methods of Critical Text Analysis in Modern Philology

Introduction: this study aimed to evaluate the influence of artificial intelligence (AI), particularly deep learning and natural language processing (NLP) technologies, on the transformation of critical text analysis in contemporary philology. Aim: the research focused on how AI-driven approaches modify traditional linguistic and literary methodologies. Methods: a qualitative literature review was conducted to examine recent academic contributions at the intersection of philology and AI. Sources were selected from peer-reviewed journals covering linguistics, computational philology, and digital humanities. Results: the analysis revealed that AI-based algorithms, especially deep learning models, enhanced the detection of latent textual structures such as lexical patterns, stylistic markers, and semantic clusters. These technologies facilitated more accurate authorship attribution and allowed for the investigation of large corpora beyond the capacities of manual analysis. However, findings indicated that while AI could identify patterns and linguistic regularities, it lacked the ability to interpret deeper cultural, emotional, and symbolic meanings embedded in literary texts. Conclusions: the integration of AI into philological research offers valuable computational tools that expand analytical possibilities without displacing the interpretive role of the human scholar. AI technologies serve as a methodological extension, enhancing the precision and scope of critical analysis. Ultimately, the use of AI enriches the study of literature by uncovering patterns inaccessible to traditional methods, while preserving the necessity of human insight for contextual and interpretative depth.